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dc.contributor.authorLin, Dahua
dc.contributor.authorGrimson, Eric
dc.contributor.authorFisher, John W., III
dc.date.accessioned2012-10-15T13:26:47Z
dc.date.available2012-10-15T13:26:47Z
dc.date.issued2010-12
dc.identifier.urihttp://hdl.handle.net/1721.1/73948
dc.description.abstractWe present a method for constructing dependent Dirichlet processes. The new approach exploits the intrinsic relationship between Dirichlet and Poisson processes in order to create a Markov chain of Dirichlet processes suitable for use as a prior over evolving mixture models. The method allows for the creation, removal, and location variation of component models over time while maintaining the property that the random measures are marginally DP distributed. Additionally, we derive a Gibbs sampling algorithm for model inference and test it on both synthetic and real data. Empirical results demonstrate that the approach is effective in estimating dynamically varying mixture models.en_US
dc.language.isoen_US
dc.publisherNeural Information Processing Systems Foundation (NIPS)en_US
dc.relation.isversionofhttp://nips.cc/Conferences/2010/Program/accepted-papers.phpen_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleConstruction of Dependent Dirichlet Processes Based on Poisson Processesen_US
dc.typeArticleen_US
dc.identifier.citationD. Lin et al. "Construction of Dependent Dirichlet Processes based on Poisson Processes" Neural Information Processing Systems 2010.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.mitauthorLin, Dahua
dc.contributor.mitauthorGrimson, Eric
dc.contributor.mitauthorFisher, John W., III
dc.relation.journalNeural Information Processing Systemsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-4844-3495
dc.identifier.orcidhttps://orcid.org/0000-0002-6192-2207
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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